Abstract
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.
Highlights
During the past few decades, enormous resources have been invested in research efforts to discover new energetic materials with improved performance, thermodynamic stability, and safety
electrotopological state (E-state) indices were previously found to be useful for predicting the sensitivity (h50 values) of energetic materials with a neural network[68]
We found that only 32 of the atom types were present in the energetic materials we studied, so we truncated it to a length of 32
Summary
During the past few decades, enormous resources have been invested in research efforts to discover new energetic materials with improved performance, thermodynamic stability, and safety. While hundreds of new energetic materials have been synthesized as a result of this research, many of which have remarkable properties, very few compounds have made it to industrial production. As a general means of developing models for relationships that have no known analytic forms, machine learning holds great promise for broad application. The evidence to date suggest machine learning models require data of sufficient quality, quantity, and diversity which ostensibly limits the application to fields in which datasets are large, organized, and/or plentiful. While machine learning has been applied to impact sensitivity[26,27,28,29], there is little or no previously published work applying ML to predict energetic properties such as explosive energy, detonation velocity, and detonation pressure. The aforementioned studies have been limited to the use of hand picked descriptors combined with linear modeling
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